This document presents the results of a study on the adoption of Sahiwal cattle breed and its impact on household farm income in Narok and Kajiado counties of Kenya. The study had three objectives: 1) Determine pastoral farmers' preferences for breeding services, 2) Estimate willingness to pay for artificial insemination services, and 3) Determine the impact of Sahiwal breed adoption on household income. Key findings include that most farmers preferred artificial insemination to natural breeding, willingness to pay for AI services was positively influenced by awareness and herd size, and Sahiwal adoption had a significant positive impact on adopters' household income compared to non-adopters. The conclusions were that promoting AI and disseminating information

1. ADOPTION OF SAHIWAL CATTLE BREED AND ITS
IMPACT ON HOUSEHOLD FARM INCOME IN NAROK
AND KAJIADO COUNTIES OF KENYA
KHAINGA DICKSON NANGABO
KM17/3006/11
SUPERVISORS
PROF GIDEON OBARE
DR ALICE MURAGE
1

2. Outline
 Introduction
 Statement of research problem
 Objectives
 Hypothesis
 Conceptual framework
 Methodology
 Results and discussion
 Conclusions and implications
2

3. Introduction
3
 In SSA - livestock ASALs supporting pastoralist livelihoods.
 According to FAO, (2001):
◦ over 80% of land is ASAL in Kenya
◦ livestock contributing 10% and 30% of total and of agricultural
GDP, respectively
◦ dairy products account for 30% of livestock GDP
 In 2011, livestock production
◦ 90% of employment and nutritional needs
◦ 95% of family incomes in ASALs

4. Cont’
4
However, pastoralists in the ASALs
o suffer high rates of malnutrition and illiteracy,
o vulnerable to regular drought and civil unrest.
 To curb this, Government, introduced Sahiwal
o adaptive to ASAL environment
o High reproductive performance
 Consequently:
o Demand for these genetic resources outstripped its supply from NSS
 Hence the need to explore;
o effective mechanisms of disseminating sahiwal genetic material and Sahiwal
adoption impact on household income

5. STATEMENT OF RESEARCH PROBLEM
Sahiwal bull
Thus the need to establish AI viability and impact of
sahiwal adoption
 Whereas demand for the sahiwal bull has
outsripped its supply, the economic assessment
for viability and implications of using AI in
pastoralist communities are not yet known.
 Sahiwal adoption impact on household income
is yet to be established.

6. Broad objective
 To investigate the contribution of Sahiwal breed on the farm income of smallholder farmers in
arid and semi-arid areas of Narok and Kajiado Counties
Specific objective
1. To determine pastoral farmers’ preferences and choices for breeding services in arid and
semi-arid areas
2. To estimate the pastoral farmers’ willingness to pay for artificial insemination services in
arid and semi-arid areas
3. To determine the impact of sahiwal breed adoption on household farm income between
sahiwal adopters and non-adopters
6

7. Hypotheses
 Farmer’s preference and choice for bulls are not significanctly different from
artificial insemination across the counties
 There is no significant difference in farmers’ willingness to pay for artificial
insemination services in arid and semi-arid areas.
 The impact of sahiwal breed adoption on household farm income is not significantly
different between sahiwal adopters and non-adopters
Conceptual framework
 Conceptual framework
7

8. Sampling procedure
 Multistage sampling
◦ Purposively Trans Mara and Kajiado districts
◦ Stratified into pure pastoralists, agro-pastoralists and ranchers in Keiyan and Lolgorian Divisions of
Transmara and Kajiado central division of Kajiado district.
◦ systematic random sampling was employed to select the sample size using DLPO’s source list
Sample size
o Farmer households formed the sampling units.
o A sample size of 384 was computed using the proportion sample size determination formula given by
Mugenda and Mugenda, (1998).
= = 384
where
n = the desired sample size of livestock farmers in Narok and Kajiado counties
z = the standard normal deviate at the required confidence level
p = the proportion in the target population estimated to have characteristics of interest
q = 1- p
d = the level of statistical significance set
Methodology

9. Objective 1. pastoral farmers’ preference and choice for breeding services
Ordered probit discrete choice model
Objective 2: Pastoral farmers’ willingness to pay for Artificial Insemination
Double-bounded dichotomous choice model
Objective 3: Impact of sahiwal adoption on household income
Propensity score matching
Empirical estimation

10. RESULTS AND DISCUSSION
10
OBJECTIVE 1: Pastoral farmers’ preference and choice for breeding services
in arid and semi-arid areas
 Most farmers had least preferred AI 68.4%, preferred 23.62% and most preference 7.98%
 Narok farmers had high preference for AI compared to Kajiado farmers and this difference was
significant.
 Choice of AI was highly influenced by its Affordability (66.87%), calf tolerance (25.77%),
accessibility (23.31%) and its success rate (23.62%)
 Difference in Choice by county based on Affordability (insignificant), success rate (insignificant)
 calf tolerance (significant), and its accessibility (significant)
 Determinants of farmers preference for AI
Positive significant influence
 group membership, access to extension, AI awareness, production system, County of residence,
years of education, household size and herd size
Negative significant influence
 farming experience and age

11. Objective 2: Pastoral farmers’ willingness to pay for Artificial
Insemination
 Mean wtp was KES 1,853.19
 Bidding was positive and significantly influenced by
 awareness,
 herd size,
 access to extension services, and
 off-farm income

12. Objective 3: Impact of sahiwal adoption on household farm income
Factors that influence Sahiwal adoption
 Positive and significant
 Age
 Labour cost
 Reproductive performance
 Negative and significant
 Herd size
 Education
Average treatment effect
 Sahiwal adopters received annual income worth KES 661,179.87 compared to KES 564,779.67
for the control group.
 This yields a difference of KES 96,400.21 in excess over the control group which translates to
KES 8,033.35 per month
 The overall effect of keeping sahiwal breed among the pastoralists is KES 213,686.49 per annum
–a monthly increment of KES 17,807. 21 in farm income over and above amount earned by non-
adopters

13. Study conclusions and implications
 Most farmers preferred AI to the bull based on AI’s low cost. It therefore implies that
promotion and Subsidization of AI services will increase its uptake and reduce the
demand for the Sahiwal bull from the NSS
 Farmers across the counties were willing to pay for AI services to meet the shortage in bull
supply. This implies that there is a potential market for AI service providers (both private
and public) which will allow pastoralist to access quality semen of well documented bulls
at cheaper price
 The impact of Sahiwal breed adoption was significant on household farm income of adopters
compared to non-adopters. This therefore means that increased dissemination of relevant
information about sahiwal breed and AI as a breeding method through extension officers and
existing farmer groups will hasten the spread of sahiwal genetic resources and increase
pastoralists farm income
13

14. Study outputs
Publication
 Ex-ante perceptions and knowledge of artificial insemination among pastoralists in Kenya.
Livestock Research for Rural Development. Vol 27:68.
http://www.lrrd.org/lrrd27/4/khai27068.html
Work in progress
 Adoption of Sahiwal cattle genetic resources and implication on household income
among Maasai pastoral communities in Kenya. Animal genetic resources
 Pastoralists’ willingness to pay for artificial insemination technology in arid and semi-arid
areas of Kenya. Journal of rural and community development
Acknowledgements
 Egerton University
 AERC and EAAP for funding the research through the CMAAE program
 Supervisors; Prof Obare, Dr Murage and Dr Ilatsia
 All the staff of AGEC/AGBM Department
 Staff of KALRO -Naivasha
 My family
 Colleagues and friends
14

15. Thank you for listening

16. Conceptual framework
Socio-economic factors (X1)
Age
Gender
Education levels
Famer’s experience
Herd size
Land size
Household size
Off-farm income
Institutional factors (X2)
Breeding policy
Farmer groups/ social capital
NGO support
Agricultural agencies (e.g.
research and marketing laws)
Extension and service provision
Credit services
Information access
Willingness to pay for breeding services (WTP)
Natural service (Bull), Artificial Insemination,
Embryo transfer
Animal Traits (X4)
Milk yield
Reproductive performance
Feeding requirements
Watering frequency
Disease tolerance
Live weight
Choice of cattle breeds (Bi)
Sahiwal, East African Zebus,
exotic breeds e.g. Friesian
Breed adoption impact
Milk and other dairy products
Live animal sales
AI perception
attributes (X3)
Accessibility
Success rate
Affordability
Offspring tolerance

17. Ordered Probit discrete choice model
According to Long (1997), ordered probit discrete choice model is defined as
iii eXY  *
(1)
where Xi refers to the observable individual specific factors,
 is a vector of parameters to be estimated and
ei is the stochastic-disturbance term with normal distribution (Greene, 2003).
Observed choice outcomes Yi are assumed to be related to the latent variable Yi
*
as:
0Y if 0
*
Y
1Y if 1
*
0 Y (2)
2Y if 2
*
1  Y
where i is unknown threshold parameter for outcome i that separate the adjacent boundary
values and is estimated together with the .'
s The estimated i , ( where i=0,1,2) follows the
order 210   .
17

18. Cont’
18
The probability that the case falls into each category j, using the estimated i parameters as
threshold limits is given as:
)(1)2Pr(
)()()1Pr(
)()0Pr(
'
1
''
1
'
XY
XXY
XY






(3)
where  represents the cumulative density function of i .
The values of  parameters were estimated by computing the marginal effects using maximum
likelihood functions defined by Greene (2003) as:



)]([
)2Pr(
)]()([
)1Pr(
)'(
)0Pr(
'
1
'
1
'
X
X
Y
XX
X
Y
X
X
Y









(4)
The estimated marginal effects indicate the change in the likelihood that a farmer would
“prefer” or “most prefer” (as opposed to least preference) AI as a result of a unit change in the
specifc explanatory variable.

19. Double-bounded dichotomous choice model
I adopt the modelling framework of Hanemann et al. (1991), where farmer responses to bidding
questions include πyy
, πnn
, πyn
, πny
, respectively.
In the first case where the respondent accepts the initial and second higher bid, we have
i
u
i BB  ;
}maxmaxPr{( , WTPBWTPandBBB u
i
u
ii
yy

}maxPr{}max|maxPr{ WTPBWTPBWTPB u
i
u
ii  (1)
}maxPr{ WTPBu
i 
In the second case where the respondent rejects the initial bid and second lower bid, we have
i
d
i BB  ;
}maxmaxPr{),( WTPBandWTPBBB d
ii
d
ii
nn
 (2)
Third case is where the respondent accepts the initial bid and rejects the second bid, we have
Bi
u
>Bi ;
}
maxPr{),( u
ii
u
ii
yn
BWTPBBB  (3)
19

20. Cont’
The last case is where the respondents rejects the initial bid and accepts the second bid, we
have i
d
i BB  ;
}maxPr{),( d
ii
d
ii
ny
BWTPBBB  (4)
Computing the mean WTP a logistic curve was specified, fitted on the data and estimated. The
log-likelihood function was then defined and estimated as:
),(ln
),(ln),(ln),(ln{)(ln
1
d
ii
nyny
i
u
ii
ynyn
i
d
ii
nnnn
i
u
ii
yy
N
i
yy
i
D
BBd
BBdBBdBBd

  
(5)
where yn
i
nn
i
yy
i ddd ,, and ny
id are binary-valued indicator variables.
The final step was to specify and estimate a WTP regression model to determine factors
influencing WTP. The regression method allows inclusion of other factors in the analysis, in
particular socioeconomic characteristics of the respondents to explain the bidding behaviour.
20

21. Propensity score matching
 Introduced in Rosenbaum and Rubin (1983)
 It pairs each program participant with a single nonparticipant, where pairs are chosen
based on the degree of similarity in the estimated probabilities of participating in the
program (the propensity scores).
 The mean impact of the program is estimated by the mean difference in the outcomes
of the matched pairs.
 Matching estimators are justified by the assumption that outcomes are independent of
program participation conditional on a set of observable characteristics
21
That is, matching assumes that there exists a set of observable conditioning variables Z for
which the nonparticipation outcome Y0 is independent of participation status D conditional on
Z.
ZDY |0  (1)
It is also assumed that for all Z there is a positive probability of participating, (D=1) or not
participating (D=0), i.e.
1)|1Pr(0  ZD (2)

22. Cont’
Assessing the impact of any intervention requires making an inference about the outcomes that
would have been observed for program participants had they not participated.
Denote Y1 the outcome conditional on participation and by Y0 the outcome conditional on non-
participation, so that the impact of participating in the program is
01 YY  (3)
Let D =1 for the group of individuals who applied and got accepted into the program for whom
Y1 is observed. Let D = 0 for persons who do not enter the program for whom Y0 is observed.
Let X denote a vector of observed individual characteristics used as conditioning variables. The
most common evaluation parameter of interest is the mean impact of treatment on the treated,
which estimate the average impact of the program among those participating in it.
)1,|()1,|(
)1,|()1,|(
01
01


DXYEDXYE
DXYYEDXΔETT
22

23. RESULTS AND DISCUSSION
OBJECTIVE 1: Pastoral farmers’ preference and choices for breeding services in arid and
semi-arid areas
Preference level Whole sample
(n=326)
Narok County
(n=193)
Kajiado County
(n=161)
𝝌 𝟐
N % N % N %
Least preferred 223 68.40 101 57.39 122 81.33 21.487
2
Preferred 77 23.62 58 32.95 19 12.67 18.475
9
Most Preferred 26 7.98 17 9.66 9 6.00 1.4773
Determinants Whole sample Narok County Kajiado County 𝜒2
N % N % N %
AI affordability 218 66.87 115 65.34 103 68.67 0.4043
AI calf tolerance 84 25.77 54 30.68 30 20.00 4.8308
AI accessibility 76 23.31 50 28.41 26 17.33 5.5567
AI success rate 77 23.62 42 23.86 35 23.33 0.0126

24. Results for marginal effects after ordered probit regression
24
AI preference
Marginal effects
(least preferred)
Marginal effects
(preferred)
Marginal effects
(most preferred)
Group membership - 0.109(0.055)** 0.094(0.047)** 0.016(0.009)*
Access extension - 0.105(0.059)* 0.089(0.049)* 0.017(0.011)*
AI awareness - 0.123(0.072)* 0.102(0.058)* 0.021(0.015)
Agro-pastoralism - 0.165(0.059)*** 0.139(0.049)*** 0.027(0.012)**
Narok County - 0.165(0.057)*** 0.140(0.049)*** 0.024(0.011)**
Nomadism - 0.138(0.085) 0.113(0.068)* 0.025(0.020)
Education - 0.131(0.020)*** 0.112(0.020)*** 0.019(0.006)***
Access credit -0.059(0.059) 0.049(0.051) 0.008(0.008)
Household size - 0.016(0.004)*** 0.014(0.004)*** 0.002(0.009)***
Herd size - 0.0004(0.0002)** 0.0003(0.000)*** 0.0001(0.00003)*
Experience 0.008(0.003)*** - 0.007(0.002)*** - 0.001(0.0005)**
Age -youth 0.190(0.060)*** - 0.168(0.055)*** - 0.022(0.009)***
Young adult 0.167(0.080)** - 0.138(0.065)** - 0.029(0.018)*
Distance - 0.001(0.003) 0.001(0.002) 0.0002(0.0004)
Attribute index - 0.602(0.177)*** 0.515(0.158)*** 0.087(0.034)**
Land size - 0.00001(0.000) 0.000001(0.000) 0.000002(0.00001)
Off-farm income 0.037(0.070) - 0.032(0.061) - 0.005(0.009)

25. Objective 2: Pastoral farmers’ willingness to pay for Artificial
Insemination
Parameter estimates for WTP model for AI with covariates
25
Variable Coefficient
Awareness 0. 683(0.242)***
Credit 0.192(0.164)
Herd size 0.001(0.001)*
Extension 0.643(0.147)***
Education 0.022(0.050)
Age -0.135(0.098)
Household size -0.010(0.013)
Off-farm income 0.533(193)***
Number of observations 384
LR χ2 119.11
Prob > χ2 0.0000
Mean WTP 1853.19

26. Objective 3: Impact of sahiwal adoption on household income
Coefficients estimates for propensity score matching using kernel matching
26
Variable Coefficient
Distance to market -0.00593(0.0135)
Middle aged farmers 1.01867(0.3805)***
Elderly farmers 1.38416(0.3462)***
County 0.11315(0.2682)
Household size -0.01931(0.0201)
Herd size -0.00212(0.0011)*
Land size -0.00013(0.0002)
Labour cost 0.00001(0.0001)*
Education -0.13626(0.0802)*
Distance to water source -0.02243(0.0263)
Watering frequency -0.02577(0.3298)
Reproductive performance 1.98787(1.1397)*
Number of observations
LR χ2
Prob> χ2
311
38.52
0.0001

27. Average treatment effect
Model results for Average Treatment Effects
27
Variable Sample Treated Controls Differenc
e
S.E. T-stat
Income Unmatched 666808.44 484748.30 182060.15 161691.90 1.13
ATT 661179.87 564779.67 96400.21 186274.30 0.52
ATU 496013.86 842904.06 346890.20
ATE 213686.49
The treated group (Sahiwal adopters) received annual income worth
KES 661,179.87 compared to KES 564779.67 for the control group.
This yields a difference of KES 96400.21 in excess over the control
group which translates to KES 8,033.35 per month.
Considering livestock production has recurrent costs that need to be
offset besides family needs and farm profit requirements, Sahiwal
keeping has proved to be more beneficial compared to the local breeds